A Strategy-Aware LLM-Based Framework for Vertiport Site Selection in Urban Air Mobility with Ground Transportation Integration

Urban air mobility (UAM) introduces electric vertical takeoff and landing (eVTOL) systems, creating new requirements for infrastructure planning. Vertiport siting is central, yet existing approaches such as multi-criteria decision analysis and optimization often rely on fixed criteria and seldom integrate ground transportation, which is critical for first- and last-mile access. Large language models (LLMs) show strong capabilities in reasoning and tool orchestration, but their role in siting tasks remains underexplored. This study proposes a strategy-aware LLM-based framework that connects heterogeneous spatial data with planning strategies expressed in natural language. A reflective loop connects the planner, executor, and validator for iterative refinement using two methods: multi-criteria decision analysis for interpretable mapping and a genetic algorithm for nonlinear optimization. Experiments in Los Angeles highlight both the potential and challenges of applying LLM agents to siting: outcome evaluation shows that strategies can be translated into distinct trade-offs, while process evaluation demonstrates the benefits of iterative refinement. The study suggests that LLM-based agents can formalize qualitative strategies into reproducible workflows, indicating their potential for UAM siting and promise for broader use in urban planning.